Data Scientist (m/f/x)

Data Scientist (m/f/x)

2.92.9 von 5 Sternen

Berlin

Link: (CompanyWebsite)

Mitarbeiteranzahl: 51 bis 200 (CompanyEmployee)

Branche: Energie- & Versorgungswirtschaft (CompanyIndustry)

Stellenbeschreibung

Anstellungsart

Vollzeit

Arbeitsort

Berlin

Leistungen

Aus der vollständigen Stellenbeschreibung

Firmenevents

Vollständige Stellenbeschreibung

At Enpal, we are not just a company; we are a movement. As a recognized greentech unicorn and one of Europes fastest-growing energy companies, were dedicated to making solar energy accessible and effortless for homeowners across the continent. Our innovative business model for solar panels, heat pumps, home energy storage systems, and EV charging stations is redefining the residential energy market. With our mission to empower homeowners to embrace clean energy, we are paving the way for a sustainable, decentralized energy future.

As the leading player in residential solar, Enpal is expanding its next frontier: Enpal Energy, our platform for managing, optimizing, and orchestrating millions of distributed energy assets. This includes building Europes largest Virtual Power Plant (VPP), connecting solar systems, batteries, heat pumps, and EV chargers to actively stabilize the grid and unlock new revenue streams for our customers.

Our vision is bold: to become the driving force in Europes transition to distributed, intelligent, and clean energy generation.

We are looking for a Data Scientist to join our growing Energy Data Science team. Our forecasting models directly support energy trading decisions across one of Europes largest residential energy communities, and your work will have measurable impact on how we participate in the energy market. This role is ideal for a hands-on data scientist with strong software engineering skills and experience contributing across the full data and ML stack.

What youll do

Research, develop, and productionize time series forecasting models for energy markets (e.g. load, generation, and weather forecasting), owning the full lifecycle from data exploration and feature engineering through model training, evaluation, deployment, monitoring, and retraining.

Contribute to our MLOps infrastructure: experiment tracking, model versioning, automated retraining pipelines, and production observability.

Communicate forecasting performance and model insights to trading and product stakeholders, connecting technical results to business impact.

Collaborate closely with Data Engineers, Software Engineers, and Product to deliver well-engineered, reliable data science products.

Help shape our data science roadmap and contribute to the direction of next-generation forecasting approaches.

Mentor and support junior data scientists, fostering a culture of continuous learning and technical growth.

What youll bring

University degree in Computer Science, Engineering, Mathematics, or a related quantitative field.

3+ years of industry experience in machine learning with a focus on time series forecasting, or in data science applied to the energy sector or complex distributed systems.

Proficiency in Python and SQL, with strong software engineering fundamentals: testing, code quality, version control, and CI/CD.

Proven experience deploying and monitoring machine learning models in production.

Practical experience with data engineering tasks: pipeline development, data quality management, and feature engineering at scale, with the ability to build, debug, and maintain these independently.

Experience with cloud infrastructure (Azure, AWS, or GCP).